SaniPath Data Analysis/ Data Discovery

Overview

Descriptive Statistics by City

Deployments Overview:

country	city	year	citylabel
Cambodia	Siem Reap	2016	Siem Reap 16
Bangladesh	Dhaka	2017	Dhaka 17
Ghana	Accra	2016	Accra 16
Zambia	Lusaka	2018	Lusaka 18
Mozambique	Maputo	2015	Maputo 15
Ghana	Accra	2018	Accra 18
Ghana	Kumasi	2018	Kumasi 18
Mozambique	Maputo	2016	Maputo 16
Uganda	Kampala	2018	Kampala 18
USA	Atlanta	2016	Atlanta 16
India	Vellore	2014	Vellore 14
Senegal	Dakar	2019	Dakar 19
Zambia	Lusaka	2019	Lusaka 19

In this database there are 13 SaniPath deployments.

Number of Neighborhoods

Neighborhoods Overview:

city	country	year	neighb_UID	neighborhood
Siem Reap	Cambodia	2016	101	Chong Kaosou
Siem Reap	Cambodia	2016	102	Kumruthemey (informal)
Siem Reap	Cambodia	2016	103	Kumruthemey (formal)
Siem Reap	Cambodia	2016	104	Steung Thumey
Siem Reap	Cambodia	2016	105	Veal/ Trapangses
Dhaka	Bangladesh	2017	201	Kalshi
Dhaka	Bangladesh	2017	202	Badda
Dhaka	Bangladesh	2017	203	Gabtoli
Dhaka	Bangladesh	2017	204	Uttarkhan
Dhaka	Bangladesh	2017	205	Gulshan
Dhaka	Bangladesh	2017	206	Kamalapur
Dhaka	Bangladesh	2017	207	Shampur
Dhaka	Bangladesh	2017	208	Hazaribagh
Dhaka	Bangladesh	2017	209	Motijhil
Dhaka	Bangladesh	2017	210	Dhanmondi
Accra	Ghana	2016	301	Shiabu
Accra	Ghana	2016	302	Chorkor
Accra	Ghana	2016	303	Kokomlemle
Accra	Ghana	2016	304	Ringway
Accra	Ghana	2016	305	Adabraka
Lusaka	Zambia	2018	401	Kanyama
Maputo	Mozambique	2015	501	Intervention
Maputo	Mozambique	2015	502	Control
Accra	Ghana	2018	601	Mataheko
Accra	Ghana	2018	602	Osu Alata
Kumasi	Ghana	2018	701	FanteNewTown
Kumasi	Ghana	2018	702	MoshieZongo
Kumasi	Ghana	2018	703	Dakodwom
Kumasi	Ghana	2018	704	Ahodwo
Maputo	Mozambique	2016	801	Intervention
Maputo	Mozambique	2016	802	Control
Kampala	Uganda	2018	901	Makindye
Kampala	Uganda	2018	902	Central
Kampala	Uganda	2018	903	Kawempe
Kampala	Uganda	2018	904	Rubaga
Kampala	Uganda	2018	905	Nakawa
Atlanta	USA	2016	1001	Peoplestown
Vellore	India	2014	1101	Old Town
Vellore	India	2014	1102	Chinna Allapuram
Dakar	Senegal	2019	1201	WakhinaneNimzatt
Dakar	Senegal	2019	1202	MedinaGounass
Dakar	Senegal	2019	1203	DTK
Dakar	Senegal	2019	1204	RufisqueEst
Dakar	Senegal	2019	1205	SicapLiberte
Lusaka	Zambia	2019	1301	Chawama
Lusaka	Zambia	2019	1302	Chazanga
Lusaka	Zambia	2019	1303	George

Overall, there are 13 deployments in 9 different countries, 10 cities, and 47 neighborhoods in this analysis.

Number of Samples per Pathway

Pathway Overview:

	Accra	Atlanta	Dakar	Dhaka	Kampala	Kumasi	Lusaka	Maputo	Siem Reap	Vellore
Open Drain Water	184		50	100	47	40	50	22
Raw Produce	90	10	50	100	50	39	50	23	33	20
Municipal Drinking Water	88	10	100	100	39	36	40	40	10	22
Ocean	40
Surface Water				100	12	3	10
Floodwater	18	7		100	50	36	30	20	50
Public Latrine	280	10		100	50	27	50	71		24
Soil	108	10	50	100	50	40	50	150	50	20
Bathing Water	9			100		21		50	10	20
Street Food	20		50	100	45	40	50
Other Drinking Water				100	39		90		150

Overall, 4053 samples were collected and analyized.

Survey Type and Number of Surveys per Deployment

Survey Type Overview:

Form	Accra	Atlanta	Dakar	Dhaka	Kampala	Kumasi	Lusaka	Maputo	Siem Reap	Vellore
Behavioral Survey
Household	1021	23	500	823	548	400	400	261	410	200
Community	30		20	28	10	16	16			8
School	20		20	35	9	16	16			8
Environmental Testing
Sample	837	47	300	1000	382	282	420	376	303	106
Lab	837	47	300	1000	382	282	420	376	303	106

In total, there were 4586 Household Surveys, 128 Community Surveys and 124 School Surveys conducted. Additionally, 4053 environmental samples were collected and subsequently analyzed in the laboratory.

Survey Type and Number of People per Deployment

Form Participants:

Form	Accra	Atlanta	Dakar	Dhaka	Kampala	Kumasi	Lusaka	Maputo	Siem Reap	Vellore
Household	1021	23	500	823	548	400	400	261	410	200
Community	420		300	501	112	240	298			117
School	435		300	597	114	320	313			151

Note: Household survey numbers include the number of surveys, whereas community and school surveys account for number of participants.

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Literature

Population Density

• Cambodia, Siem Reap:
• Bangladesh, Dhaka:
  Census 2011, Report: Female/ Male Population (Table C02), Population Density (Table C01)
• Ghana, Accra:
  Census 2010, Report:
  Greater Accra Region: 1235.8 population density in 2010, 895.5 population density in 2000 (people per kmsq) (Table 4.4).
  90% of the population is considered urban population (Table 4.5).
  
• Zambia, Lusaka:
  Census 2010, Report: Population density for Lusaka 63.5 (2000) and 100.1 (2010) people per kmsq (Table 2.7).
  Census 2010, Population.
• Mozambique, Maputo:
  Note: added population information to meta_neighborhood.csv file. Sources: Ghana Statistical Services - Census 2010 Shapefile from Habib.

Enteric Disease Burden

Fecal Sludge Managment

Shit Flow Diagram

• Cambodia, Siem Reap:
  no SFD available
• Bangladesh, Dhaka:
  SFD Link. The SFD report from March 2016 indicates that less than 1% of fecal sludge in Dhaka is safely managed.
• Ghana, Accra:
  no SFD available
• Zambia, Lusaka:
  SFD Link. The SFD report from September 2018 shows that 17% of fecal sludge is safely managed.
• Mozambique, Maputo:
  no SFD available
• Ghana, Kumasi:
  SFD Link. The SFD report from November 2015 shows that 55% of fecal sludge is safely managed.

WASH Interventions in the past 10 Years

Local Stakeholders/ Decision Makers

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MF vs. IDEXX

How many samples were collected for each deployment and each pathway by laboratory analysis method: Membrane Filtration (MF) or IDEXX Quanti-Tray (IDEXX).

Lab Method used:

	IDEXX	MF
Accra	0	837
Atlanta	47	0
Dakar	0	300
Dhaka	1000	0
Kampala	0	382
Kumasi	0	282
Lusaka	420	0
Maputo	0	376
Siem Reap	0	303
Vellore	0	106

Lab Method per Sample Type:

	IDEXX	MF
Open Drain Water	150	343
Raw Produce	160	305
Municipal Drinking Water	150	335
Ocean	0	40
Surface Water	110	15
Floodwater	137	174
Public Latrine	160	452
Soil	160	468
Bathing Water	100	110
Street Food	150	155
Other Drinking Water	190	189

Membrane Filtration: valid vs. invalid test results

How many samples tested with the Membrane Filtration method produced valid or invalid results?
Note: Invalid MF results include TDTC (Too Dirty To Count) classifications of laboratory results of samples. Valid MF results are all results between the range of 0 and 200 as well as TNTC (Too Numerous To Count).

Overview: Valid Membrane Filtration Lab Results

n	valid	invalid	percent valid
6030	5846	184	96.95

Valid Membrane Filtration Lab Results by Deployment

city	n	valid	invalid	percent valid
Accra	1905	1874	31	98.37
Dakar	602	601	1	99.83
Kampala	947	924	23	97.57
Kumasi	708	670	38	94.63
Maputo	917	840	77	91.6
Siem Reap	739	725	14	98.11
Vellore	212	212	0	100

Valid Membrane Filtration Lab Results by Sample Type

sample type	n	valid	invalid	percent valid
Open Drain Water	877	872	5	99.43
Raw Produce	770	742	28	96.36
Municipal Drinking Water	671	671	0	100
Ocean	80	80	0	100
Surface Water	32	31	1	96.88
Floodwater	495	485	10	97.98
Public Latrine	910	890	20	97.8
Soil	1185	1073	112	90.55
Bathing Water	220	219	1	99.55
Street Food	410	408	2	99.51
Other Drinking Water	380	375	5	98.68

Valid Membrane Filtration Lab Results by city and Sample Type

city	sample type	n	valid	invalid	percent valid
Accra	Open Drain Water	531	531	0	100
Accra	Raw Produce	199	194	5	97.49
Accra	Municipal Drinking Water	176	176	0	100
Accra	Ocean	80	80	0	100
Accra	Floodwater	45	45	0	100
Accra	Public Latrine	560	545	15	97.32
Accra	Soil	236	225	11	95.34
Accra	Bathing Water	18	18	0	100
Accra	Street Food	60	60	0	100
Dakar	Open Drain Water	100	99	1	99
Dakar	Raw Produce	100	100	0	100
Dakar	Municipal Drinking Water	201	201	0	100
Dakar	Soil	100	100	0	100
Dakar	Street Food	101	101	0	100
Kampala	Open Drain Water	100	97	3	97
Kampala	Raw Produce	146	142	4	97.26
Kampala	Municipal Drinking Water	78	78	0	100
Kampala	Surface Water	26	25	1	96.15
Kampala	Floodwater	143	143	0	100
Kampala	Public Latrine	106	102	4	96.23
Kampala	Soil	139	129	10	92.81
Kampala	Street Food	129	128	1	99.22
Kampala	Other Drinking Water	80	80	0	100
Kumasi	Open Drain Water	80	79	1	98.75
Kumasi	Raw Produce	117	105	12	89.74
Kumasi	Municipal Drinking Water	72	72	0	100
Kumasi	Surface Water	6	6	0	100
Kumasi	Floodwater	97	94	3	96.91
Kumasi	Public Latrine	54	53	1	98.15
Kumasi	Soil	120	101	19	84.17
Kumasi	Bathing Water	42	41	1	97.62
Kumasi	Street Food	120	119	1	99.17
Maputo	Open Drain Water	66	66	0	100
Maputo	Raw Produce	69	67	2	97.1
Maputo	Municipal Drinking Water	80	80	0	100
Maputo	Floodwater	60	54	6	90
Maputo	Public Latrine	142	142	0	100
Maputo	Soil	400	331	69	82.75
Maputo	Bathing Water	100	100	0	100
Siem Reap	Raw Produce	99	94	5	94.95
Siem Reap	Municipal Drinking Water	20	20	0	100
Siem Reap	Floodwater	150	149	1	99.33
Siem Reap	Soil	150	147	3	98
Siem Reap	Bathing Water	20	20	0	100
Siem Reap	Other Drinking Water	300	295	5	98.33
Vellore	Raw Produce	40	40	0	100
Vellore	Municipal Drinking Water	44	44	0	100
Vellore	Public Latrine	48	48	0	100
Vellore	Soil	40	40	0	100
Vellore	Bathing Water	40	40	0	100

These results suggest sample types with a low percentage of validity of MF testing could improve the overall data quality by addressing potential sampling problems in training. Numbers in the “Percent Valid” column in RED indicate a rate below 80%.

IDEXX: valid vs. invalid test results

How many samples tested with IDEXX method produced a valid or invalid result?

Valid IDEXX Lab Results

n	valid	invalid	percent valid
3668	3667	1	99.97

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Dilution Error

When a dilution error occurs, meaning that a sample was not properly processed in the laboratory, the SaniPath Tool automatically discards the E. coli results of this sample (e.g. when the E. coli count for the second dilution is higher than the first dilution - including a variety of possible combinations of illogical results and potential errors).
This section explores how many samples were lost due to dilution error.

Dilution Error Overview:

Dilution Error by City

city	omitted	n	percentage
Accra	18	837	2.15
Atlanta	0	47	0.00
Dakar	15	300	5.00
Dhaka	53	1000	5.30
Kampala	46	382	12.04
Kumasi	3	282	1.06
Lusaka	20	420	4.76
Maputo	22	376	5.85
Siem Reap	5	303	1.65
Vellore	9	106	8.49

Dilution Error By city, by neighborhood

Dilution Error by City and Neighborhood

city	neighb_UID	omitted	n	percentage
Accra	301	3	93	3.23
Accra	302	4	253	1.58
Accra	303	7	124	5.65
Accra	304	2	85	2.35
Accra	305	2	133	1.50
Dakar	1201	3	60	5.00
Dakar	1202	1	60	1.67
Dakar	1203	2	60	3.33
Dakar	1204	8	60	13.33
Dakar	1205	1	60	1.67
Dhaka	201	8	100	8.00
Dhaka	202	2	100	2.00
Dhaka	203	3	100	3.00
Dhaka	204	9	100	9.00
Dhaka	205	2	100	2.00
Dhaka	206	6	100	6.00
Dhaka	207	3	100	3.00
Dhaka	208	6	100	6.00
Dhaka	209	10	100	10.00
Dhaka	210	4	100	4.00
Kampala	901	14	67	20.90
Kampala	902	9	78	11.54
Kampala	903	2	78	2.56
Kampala	904	15	77	19.48
Kampala	905	6	82	7.32
Kumasi	701	2	68	2.94
Kumasi	702	1	79	1.27
Lusaka	401	18	170	10.59
Lusaka	1301	2	90	2.22
Maputo	501	7	98	7.14
Maputo	502	5	54	9.26
Maputo	801	8	152	5.26
Maputo	802	2	72	2.78
Siem Reap	101	2	70	2.86
Siem Reap	102	2	60	3.33
Siem Reap	103	1	49	2.04
Vellore	1101	2	53	3.77
Vellore	1102	7	53	13.21

Dilution Error By city, by neighborhood, by sample

Dilution Error by City and Neighborhood and Sample Type

city	neighb_UID	sample_type_name	omitted	n	percentage
Accra	301	Open Drain Water	1	16	6.25
Accra	301	Raw Produce	1	20	5.00
Accra	301	Soil	1	18	5.56
Accra	302	Raw Produce	2	20	10.00
Accra	302	Public Latrine	1	95	1.05
Accra	302	Soil	1	40	2.50
Accra	303	Raw Produce	5	9	55.56
Accra	303	Public Latrine	2	59	3.39
Accra	304	Raw Produce	1	10	10.00
Accra	304	Public Latrine	1	29	3.45
Accra	305	Raw Produce	2	11	18.18
Dakar	1201	Soil	2	10	20.00
Dakar	1201	Street Food	1	10	10.00
Dakar	1202	Open Drain Water	1	10	10.00
Dakar	1203	Open Drain Water	1	10	10.00
Dakar	1203	Soil	1	10	10.00
Dakar	1204	Open Drain Water	7	10	70.00
Dakar	1204	Soil	1	10	10.00
Dakar	1205	Soil	1	10	10.00
Dhaka	201	Open Drain Water	1	10	10.00
Dhaka	201	Raw Produce	1	10	10.00
Dhaka	201	Floodwater	2	10	20.00
Dhaka	201	Soil	2	10	20.00
Dhaka	201	Street Food	2	10	20.00
Dhaka	202	Raw Produce	2	10	20.00
Dhaka	203	Raw Produce	1	10	10.00
Dhaka	203	Soil	1	10	10.00
Dhaka	203	Street Food	1	10	10.00
Dhaka	204	Raw Produce	2	10	20.00
Dhaka	204	Surface Water	1	10	10.00
Dhaka	204	Floodwater	2	10	20.00
Dhaka	204	Soil	1	10	10.00
Dhaka	204	Street Food	3	10	30.00
Dhaka	205	Floodwater	1	10	10.00
Dhaka	205	Street Food	1	10	10.00
Dhaka	206	Raw Produce	2	10	20.00
Dhaka	206	Surface Water	1	10	10.00
Dhaka	206	Floodwater	1	10	10.00
Dhaka	206	Soil	1	10	10.00
Dhaka	206	Street Food	1	10	10.00
Dhaka	207	Raw Produce	2	10	20.00
Dhaka	207	Soil	1	10	10.00
Dhaka	208	Raw Produce	1	10	10.00
Dhaka	208	Floodwater	1	10	10.00
Dhaka	208	Soil	2	10	20.00
Dhaka	208	Street Food	2	10	20.00
Dhaka	209	Raw Produce	2	10	20.00
Dhaka	209	Surface Water	2	10	20.00
Dhaka	209	Soil	2	10	20.00
Dhaka	209	Street Food	4	10	40.00
Dhaka	210	Surface Water	1	10	10.00
Dhaka	210	Soil	1	10	10.00
Dhaka	210	Street Food	2	10	20.00
Kampala	901	Raw Produce	1	10	10.00
Kampala	901	Surface Water	3	8	37.50
Kampala	901	Public Latrine	3	10	30.00
Kampala	901	Soil	2	10	20.00
Kampala	901	Street Food	5	10	50.00
Kampala	902	Raw Produce	4	10	40.00
Kampala	902	Soil	1	10	10.00
Kampala	902	Street Food	3	9	33.33
Kampala	902	Other Drinking Water	1	10	10.00
Kampala	903	Raw Produce	1	10	10.00
Kampala	903	Soil	1	10	10.00
Kampala	904	Raw Produce	3	10	30.00
Kampala	904	Floodwater	3	10	30.00
Kampala	904	Soil	4	10	40.00
Kampala	904	Street Food	4	10	40.00
Kampala	904	Other Drinking Water	1	10	10.00
Kampala	905	Floodwater	1	10	10.00
Kampala	905	Soil	2	10	20.00
Kampala	905	Other Drinking Water	3	10	30.00
Kumasi	701	Raw Produce	1	9	11.11
Kumasi	701	Street Food	1	10	10.00
Kumasi	702	Public Latrine	1	9	11.11
Lusaka	401	Open Drain Water	2	20	10.00
Lusaka	401	Raw Produce	1	20	5.00
Lusaka	401	Floodwater	10	30	33.33
Lusaka	401	Soil	4	20	20.00
Lusaka	401	Street Food	1	20	5.00
Lusaka	1301	Soil	2	10	20.00
Maputo	501	Open Drain Water	1	9	11.11
Maputo	501	Soil	5	54	9.26
Maputo	501	Bathing Water	1	15	6.67
Maputo	502	Open Drain Water	1	6	16.67
Maputo	502	Floodwater	2	5	40.00
Maputo	502	Soil	2	20	10.00
Maputo	801	Municipal Drinking Water	1	30	3.33
Maputo	801	Public Latrine	5	30	16.67
Maputo	801	Soil	1	57	1.75
Maputo	801	Bathing Water	1	15	6.67
Maputo	802	Raw Produce	1	8	12.50
Maputo	802	Municipal Drinking Water	1	10	10.00
Siem Reap	101	Raw Produce	2	10	20.00
Siem Reap	102	Raw Produce	2	10	20.00
Siem Reap	103	Other Drinking Water	1	29	3.45
Vellore	1101	Municipal Drinking Water	2	11	18.18
Vellore	1102	Municipal Drinking Water	2	11	18.18
Vellore	1102	Public Latrine	1	12	8.33
Vellore	1102	Soil	1	10	10.00
Vellore	1102	Bathing Water	3	10	30.00

Sample Size Minimum for Model:

To correctly perform the QMRA calculations, a minimum sample size of 10 samples has to be met. Which sample types per neighborhood and deployment have fulfilled the requirement? (omitted = sample discarded because of error)

Number of pathways that did not meet the requred 10 sample minimum, per deployment:

Requirements Not Met - Overview

citylabel	neighb_UID	not_met
Maputo 15	501	2
Maputo 15	502	2
Accra 16	303	2
Accra 16	304	1
Accra 16	305	1
Maputo 16	801	1
Maputo 16	802	3
Siem Reap 16	105	1
Atlanta 16	1001	1
Accra 18	601	2
Accra 18	602	2
Kumasi 18	701	3
Kumasi 18	702	1
Kumasi 18	703	3
Kumasi 18	704	2
Kampala 18	901	2
Kampala 18	902	2
Kampala 18	903	4
Kampala 18	904	1

Minimum Requirement Met?

citylabel	neighb_UID	sample_type_name	omitted	n	requ_met	net_n	net_requ_met
Vellore 14	1101	Raw Produce	0	10	Yes	10	Yes
Vellore 14	1101	Municipal Drinking Water	2	11	Yes	9	No
Vellore 14	1101	Public Latrine	0	12	Yes	12	Yes
Vellore 14	1101	Soil	0	10	Yes	10	Yes
Vellore 14	1101	Bathing Water	0	10	Yes	10	Yes
Vellore 14	1102	Raw Produce	0	10	Yes	10	Yes
Vellore 14	1102	Municipal Drinking Water	2	11	Yes	9	No
Vellore 14	1102	Public Latrine	1	12	Yes	11	Yes
Vellore 14	1102	Soil	1	10	Yes	9	No
Vellore 14	1102	Bathing Water	3	10	Yes	7	No
Maputo 15	501	Open Drain Water	1	9	No	8	No
Maputo 15	501	Floodwater	0	5	No	5	No
Maputo 15	501	Public Latrine	0	15	Yes	15	Yes
Maputo 15	501	Soil	5	54	Yes	49	Yes
Maputo 15	501	Bathing Water	1	15	Yes	14	Yes
Maputo 15	502	Open Drain Water	1	6	No	5	No
Maputo 15	502	Floodwater	2	5	No	3	No
Maputo 15	502	Public Latrine	0	13	Yes	13	Yes
Maputo 15	502	Soil	2	20	Yes	18	Yes
Maputo 15	502	Bathing Water	0	10	Yes	10	Yes
Accra 16	301	Open Drain Water	1	16	Yes	15	Yes
Accra 16	301	Raw Produce	1	20	Yes	19	Yes
Accra 16	301	Municipal Drinking Water	0	10	Yes	10	Yes
Accra 16	301	Ocean	0	10	Yes	10	Yes
Accra 16	301	Public Latrine	0	19	Yes	19	Yes
Accra 16	301	Soil	1	18	Yes	17	Yes
Accra 16	302	Open Drain Water	0	48	Yes	48	Yes
Accra 16	302	Raw Produce	2	20	Yes	18	Yes
Accra 16	302	Municipal Drinking Water	0	30	Yes	30	Yes
Accra 16	302	Ocean	0	20	Yes	20	Yes
Accra 16	302	Public Latrine	1	95	Yes	94	Yes
Accra 16	302	Soil	1	40	Yes	39	Yes
Accra 16	303	Open Drain Water	0	37	Yes	37	Yes
Accra 16	303	Raw Produce	5	9	No	4	No
Accra 16	303	Municipal Drinking Water	0	9	No	9	No
Accra 16	303	Public Latrine	2	59	Yes	57	Yes
Accra 16	303	Soil	0	10	Yes	10	Yes
Accra 16	304	Open Drain Water	0	27	Yes	27	Yes
Accra 16	304	Raw Produce	1	10	Yes	9	No
Accra 16	304	Municipal Drinking Water	0	9	No	9	No
Accra 16	304	Public Latrine	1	29	Yes	28	Yes
Accra 16	304	Soil	0	10	Yes	10	Yes
Accra 16	305	Open Drain Water	0	35	Yes	35	Yes
Accra 16	305	Raw Produce	2	11	Yes	9	No
Accra 16	305	Municipal Drinking Water	0	10	Yes	10	Yes
Accra 16	305	Floodwater	0	9	No	9	No
Accra 16	305	Public Latrine	0	58	Yes	58	Yes
Accra 16	305	Soil	0	10	Yes	10	Yes
Maputo 16	801	Raw Produce	0	15	Yes	15	Yes
Maputo 16	801	Municipal Drinking Water	1	30	Yes	29	Yes
Maputo 16	801	Floodwater	0	5	No	5	No
Maputo 16	801	Public Latrine	5	30	Yes	25	Yes
Maputo 16	801	Soil	1	57	Yes	56	Yes
Maputo 16	801	Bathing Water	1	15	Yes	14	Yes
Maputo 16	802	Open Drain Water	0	7	No	7	No
Maputo 16	802	Raw Produce	1	8	No	7	No
Maputo 16	802	Municipal Drinking Water	1	10	Yes	9	No
Maputo 16	802	Floodwater	0	5	No	5	No
Maputo 16	802	Public Latrine	0	13	Yes	13	Yes
Maputo 16	802	Soil	0	19	Yes	19	Yes
Maputo 16	802	Bathing Water	0	10	Yes	10	Yes
Siem Reap 16	101	Raw Produce	2	10	Yes	8	No
Siem Reap 16	101	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	101	Soil	0	10	Yes	10	Yes
Siem Reap 16	101	Bathing Water	0	10	Yes	10	Yes
Siem Reap 16	101	Other Drinking Water	0	30	Yes	30	Yes
Siem Reap 16	102	Raw Produce	2	10	Yes	8	No
Siem Reap 16	102	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	102	Soil	0	10	Yes	10	Yes
Siem Reap 16	102	Other Drinking Water	0	30	Yes	30	Yes
Siem Reap 16	103	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	103	Soil	0	10	Yes	10	Yes
Siem Reap 16	103	Other Drinking Water	1	29	Yes	28	Yes
Siem Reap 16	104	Raw Produce	0	10	Yes	10	Yes
Siem Reap 16	104	Municipal Drinking Water	0	10	Yes	10	Yes
Siem Reap 16	104	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	104	Soil	0	10	Yes	10	Yes
Siem Reap 16	104	Other Drinking Water	0	31	Yes	31	Yes
Siem Reap 16	105	Raw Produce	0	3	No	3	No
Siem Reap 16	105	Floodwater	0	10	Yes	10	Yes
Siem Reap 16	105	Soil	0	10	Yes	10	Yes
Siem Reap 16	105	Other Drinking Water	0	30	Yes	30	Yes
Atlanta 16	1001	Raw Produce	0	10	Yes	10	Yes
Atlanta 16	1001	Municipal Drinking Water	0	10	Yes	10	Yes
Atlanta 16	1001	Floodwater	0	7	No	7	No
Atlanta 16	1001	Public Latrine	0	10	Yes	10	Yes
Atlanta 16	1001	Soil	0	10	Yes	10	Yes
Dhaka 17	201	Open Drain Water	1	10	Yes	9	No
Dhaka 17	201	Raw Produce	1	10	Yes	9	No
Dhaka 17	201	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	201	Surface Water	0	10	Yes	10	Yes
Dhaka 17	201	Floodwater	2	10	Yes	8	No
Dhaka 17	201	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	201	Soil	2	10	Yes	8	No
Dhaka 17	201	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	201	Street Food	2	10	Yes	8	No
Dhaka 17	201	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	202	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	202	Raw Produce	2	10	Yes	8	No
Dhaka 17	202	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	202	Surface Water	0	10	Yes	10	Yes
Dhaka 17	202	Floodwater	0	10	Yes	10	Yes
Dhaka 17	202	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	202	Soil	0	10	Yes	10	Yes
Dhaka 17	202	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	202	Street Food	0	10	Yes	10	Yes
Dhaka 17	202	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	203	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	203	Raw Produce	1	10	Yes	9	No
Dhaka 17	203	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	203	Surface Water	0	10	Yes	10	Yes
Dhaka 17	203	Floodwater	0	10	Yes	10	Yes
Dhaka 17	203	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	203	Soil	1	10	Yes	9	No
Dhaka 17	203	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	203	Street Food	1	10	Yes	9	No
Dhaka 17	203	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	204	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	204	Raw Produce	2	10	Yes	8	No
Dhaka 17	204	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	204	Surface Water	1	10	Yes	9	No
Dhaka 17	204	Floodwater	2	10	Yes	8	No
Dhaka 17	204	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	204	Soil	1	10	Yes	9	No
Dhaka 17	204	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	204	Street Food	3	10	Yes	7	No
Dhaka 17	204	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	205	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	205	Raw Produce	0	10	Yes	10	Yes
Dhaka 17	205	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	205	Surface Water	0	10	Yes	10	Yes
Dhaka 17	205	Floodwater	1	10	Yes	9	No
Dhaka 17	205	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	205	Soil	0	10	Yes	10	Yes
Dhaka 17	205	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	205	Street Food	1	10	Yes	9	No
Dhaka 17	205	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	206	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	206	Raw Produce	2	10	Yes	8	No
Dhaka 17	206	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	206	Surface Water	1	10	Yes	9	No
Dhaka 17	206	Floodwater	1	10	Yes	9	No
Dhaka 17	206	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	206	Soil	1	10	Yes	9	No
Dhaka 17	206	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	206	Street Food	1	10	Yes	9	No
Dhaka 17	206	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	207	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	207	Raw Produce	2	10	Yes	8	No
Dhaka 17	207	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	207	Surface Water	0	10	Yes	10	Yes
Dhaka 17	207	Floodwater	0	10	Yes	10	Yes
Dhaka 17	207	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	207	Soil	1	10	Yes	9	No
Dhaka 17	207	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	207	Street Food	0	10	Yes	10	Yes
Dhaka 17	207	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	208	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	208	Raw Produce	1	10	Yes	9	No
Dhaka 17	208	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	208	Surface Water	0	10	Yes	10	Yes
Dhaka 17	208	Floodwater	1	10	Yes	9	No
Dhaka 17	208	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	208	Soil	2	10	Yes	8	No
Dhaka 17	208	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	208	Street Food	2	10	Yes	8	No
Dhaka 17	208	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	209	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	209	Raw Produce	2	10	Yes	8	No
Dhaka 17	209	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	209	Surface Water	2	10	Yes	8	No
Dhaka 17	209	Floodwater	0	10	Yes	10	Yes
Dhaka 17	209	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	209	Soil	2	10	Yes	8	No
Dhaka 17	209	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	209	Street Food	4	10	Yes	6	No
Dhaka 17	209	Other Drinking Water	0	10	Yes	10	Yes
Dhaka 17	210	Open Drain Water	0	10	Yes	10	Yes
Dhaka 17	210	Raw Produce	0	10	Yes	10	Yes
Dhaka 17	210	Municipal Drinking Water	0	10	Yes	10	Yes
Dhaka 17	210	Surface Water	1	10	Yes	9	No
Dhaka 17	210	Floodwater	0	10	Yes	10	Yes
Dhaka 17	210	Public Latrine	0	10	Yes	10	Yes
Dhaka 17	210	Soil	1	10	Yes	9	No
Dhaka 17	210	Bathing Water	0	10	Yes	10	Yes
Dhaka 17	210	Street Food	2	10	Yes	8	No
Dhaka 17	210	Other Drinking Water	0	10	Yes	10	Yes
Lusaka 18	401	Open Drain Water	2	20	Yes	18	Yes
Lusaka 18	401	Raw Produce	1	20	Yes	19	Yes
Lusaka 18	401	Municipal Drinking Water	0	10	Yes	10	Yes
Lusaka 18	401	Floodwater	10	30	Yes	20	Yes
Lusaka 18	401	Public Latrine	0	20	Yes	20	Yes
Lusaka 18	401	Soil	4	20	Yes	16	Yes
Lusaka 18	401	Street Food	1	20	Yes	19	Yes
Lusaka 18	401	Other Drinking Water	0	30	Yes	30	Yes
Accra 18	601	Open Drain Water	0	10	Yes	10	Yes
Accra 18	601	Raw Produce	0	10	Yes	10	Yes
Accra 18	601	Municipal Drinking Water	0	10	Yes	10	Yes
Accra 18	601	Floodwater	0	4	No	4	No
Accra 18	601	Public Latrine	0	10	Yes	10	Yes
Accra 18	601	Soil	0	10	Yes	10	Yes
Accra 18	601	Bathing Water	0	4	No	4	No
Accra 18	601	Street Food	0	10	Yes	10	Yes
Accra 18	602	Open Drain Water	0	11	Yes	11	Yes
Accra 18	602	Raw Produce	0	10	Yes	10	Yes
Accra 18	602	Municipal Drinking Water	0	10	Yes	10	Yes
Accra 18	602	Ocean	0	10	Yes	10	Yes
Accra 18	602	Floodwater	0	5	No	5	No
Accra 18	602	Public Latrine	0	10	Yes	10	Yes
Accra 18	602	Soil	0	10	Yes	10	Yes
Accra 18	602	Bathing Water	0	5	No	5	No
Accra 18	602	Street Food	0	10	Yes	10	Yes
Kumasi 18	701	Open Drain Water	0	10	Yes	10	Yes
Kumasi 18	701	Raw Produce	1	9	No	8	No
Kumasi 18	701	Municipal Drinking Water	0	10	Yes	10	Yes
Kumasi 18	701	Floodwater	0	6	No	6	No
Kumasi 18	701	Public Latrine	0	10	Yes	10	Yes
Kumasi 18	701	Soil	0	10	Yes	10	Yes
Kumasi 18	701	Bathing Water	0	3	No	3	No
Kumasi 18	701	Street Food	1	10	Yes	9	No
Kumasi 18	702	Open Drain Water	0	10	Yes	10	Yes
Kumasi 18	702	Raw Produce	0	10	Yes	10	Yes
Kumasi 18	702	Municipal Drinking Water	0	10	Yes	10	Yes
Kumasi 18	702	Floodwater	0	10	Yes	10	Yes
Kumasi 18	702	Public Latrine	1	9	No	8	No
Kumasi 18	702	Soil	0	10	Yes	10	Yes
Kumasi 18	702	Bathing Water	0	10	Yes	10	Yes
Kumasi 18	702	Street Food	0	10	Yes	10	Yes
Kumasi 18	703	Open Drain Water	0	10	Yes	10	Yes
Kumasi 18	703	Raw Produce	0	10	Yes	10	Yes
Kumasi 18	703	Municipal Drinking Water	0	10	Yes	10	Yes
Kumasi 18	703	Surface Water	0	3	No	3	No
Kumasi 18	703	Floodwater	0	10	Yes	10	Yes
Kumasi 18	703	Public Latrine	0	4	No	4	No
Kumasi 18	703	Soil	0	10	Yes	10	Yes
Kumasi 18	703	Bathing Water	0	8	No	8	No
Kumasi 18	703	Street Food	0	10	Yes	10	Yes
Kumasi 18	704	Open Drain Water	0	10	Yes	10	Yes
Kumasi 18	704	Raw Produce	0	10	Yes	10	Yes
Kumasi 18	704	Municipal Drinking Water	0	6	No	6	No
Kumasi 18	704	Floodwater	0	10	Yes	10	Yes
Kumasi 18	704	Public Latrine	0	4	No	4	No
Kumasi 18	704	Soil	0	10	Yes	10	Yes
Kumasi 18	704	Street Food	0	10	Yes	10	Yes
Kampala 18	901	Open Drain Water	0	9	No	9	No
Kampala 18	901	Raw Produce	1	10	Yes	9	No
Kampala 18	901	Surface Water	3	8	No	5	No
Kampala 18	901	Floodwater	0	10	Yes	10	Yes
Kampala 18	901	Public Latrine	3	10	Yes	7	No
Kampala 18	901	Soil	2	10	Yes	8	No
Kampala 18	901	Street Food	5	10	Yes	5	No
Kampala 18	902	Open Drain Water	0	9	No	9	No
Kampala 18	902	Raw Produce	4	10	Yes	6	No
Kampala 18	902	Municipal Drinking Water	0	10	Yes	10	Yes
Kampala 18	902	Floodwater	0	10	Yes	10	Yes
Kampala 18	902	Public Latrine	0	10	Yes	10	Yes
Kampala 18	902	Soil	1	10	Yes	9	No
Kampala 18	902	Street Food	3	9	No	6	No
Kampala 18	902	Other Drinking Water	1	10	Yes	9	No
Kampala 18	903	Open Drain Water	0	10	Yes	10	Yes
Kampala 18	903	Raw Produce	1	10	Yes	9	No
Kampala 18	903	Municipal Drinking Water	0	9	No	9	No
Kampala 18	903	Surface Water	0	4	No	4	No
Kampala 18	903	Floodwater	0	10	Yes	10	Yes
Kampala 18	903	Public Latrine	0	10	Yes	10	Yes
Kampala 18	903	Soil	1	10	Yes	9	No
Kampala 18	903	Street Food	0	6	No	6	No
Kampala 18	903	Other Drinking Water	0	9	No	9	No
Kampala 18	904	Open Drain Water	0	7	No	7	No
Kampala 18	904	Raw Produce	3	10	Yes	7	No
Kampala 18	904	Municipal Drinking Water	0	10	Yes	10	Yes
Kampala 18	904	Floodwater	3	10	Yes	7	No
Kampala 18	904	Public Latrine	0	10	Yes	10	Yes
Kampala 18	904	Soil	4	10	Yes	6	No
Kampala 18	904	Street Food	4	10	Yes	6	No
Kampala 18	904	Other Drinking Water	1	10	Yes	9	No
Kampala 18	905	Open Drain Water	0	12	Yes	12	Yes
Kampala 18	905	Raw Produce	0	10	Yes	10	Yes
Kampala 18	905	Municipal Drinking Water	0	10	Yes	10	Yes
Kampala 18	905	Floodwater	1	10	Yes	9	No
Kampala 18	905	Public Latrine	0	10	Yes	10	Yes
Kampala 18	905	Soil	2	10	Yes	8	No
Kampala 18	905	Street Food	0	10	Yes	10	Yes
Kampala 18	905	Other Drinking Water	3	10	Yes	7	No
Lusaka 19	1301	Open Drain Water	0	10	Yes	10	Yes
Lusaka 19	1301	Raw Produce	0	10	Yes	10	Yes
Lusaka 19	1301	Municipal Drinking Water	0	10	Yes	10	Yes
Lusaka 19	1301	Surface Water	0	10	Yes	10	Yes
Lusaka 19	1301	Public Latrine	0	10	Yes	10	Yes
Lusaka 19	1301	Soil	2	10	Yes	8	No
Lusaka 19	1301	Street Food	0	10	Yes	10	Yes
Lusaka 19	1301	Other Drinking Water	0	20	Yes	20	Yes
Lusaka 19	1302	Open Drain Water	0	10	Yes	10	Yes
Lusaka 19	1302	Raw Produce	0	10	Yes	10	Yes
Lusaka 19	1302	Municipal Drinking Water	0	10	Yes	10	Yes
Lusaka 19	1302	Public Latrine	0	10	Yes	10	Yes
Lusaka 19	1302	Soil	0	10	Yes	10	Yes
Lusaka 19	1302	Street Food	0	10	Yes	10	Yes
Lusaka 19	1302	Other Drinking Water	0	20	Yes	20	Yes
Lusaka 19	1303	Open Drain Water	0	10	Yes	10	Yes
Lusaka 19	1303	Raw Produce	0	10	Yes	10	Yes
Lusaka 19	1303	Municipal Drinking Water	0	10	Yes	10	Yes
Lusaka 19	1303	Public Latrine	0	10	Yes	10	Yes
Lusaka 19	1303	Soil	0	10	Yes	10	Yes
Lusaka 19	1303	Street Food	0	10	Yes	10	Yes
Lusaka 19	1303	Other Drinking Water	0	20	Yes	20	Yes
Dakar 19	1201	Open Drain Water	0	10	Yes	10	Yes
Dakar 19	1201	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1201	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1201	Soil	2	10	Yes	8	No
Dakar 19	1201	Street Food	1	10	Yes	9	No
Dakar 19	1202	Open Drain Water	1	10	Yes	9	No
Dakar 19	1202	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1202	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1202	Soil	0	10	Yes	10	Yes
Dakar 19	1202	Street Food	0	10	Yes	10	Yes
Dakar 19	1203	Open Drain Water	1	10	Yes	9	No
Dakar 19	1203	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1203	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1203	Soil	1	10	Yes	9	No
Dakar 19	1203	Street Food	0	10	Yes	10	Yes
Dakar 19	1204	Open Drain Water	7	10	Yes	3	No
Dakar 19	1204	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1204	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1204	Soil	1	10	Yes	9	No
Dakar 19	1204	Street Food	0	10	Yes	10	Yes
Dakar 19	1205	Open Drain Water	0	10	Yes	10	Yes
Dakar 19	1205	Raw Produce	0	10	Yes	10	Yes
Dakar 19	1205	Municipal Drinking Water	0	20	Yes	20	Yes
Dakar 19	1205	Soil	1	10	Yes	9	No
Dakar 19	1205	Street Food	0	10	Yes	10	Yes

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Sample Types

E. coli count per Sample Types

sample_type_name	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Open Drain Water	493	-0.30	9.58	7.69	8.39	16.77
Raw Produce	465	1.40	7.00	5.29	6.04	12.08
Municipal Drinking Water	485	-0.30	4.76	2.70	3.55	7.10
Ocean	40	1.95	6.30	5.59	5.79	11.59
Surface Water	125	1.30	7.38	5.99	6.49	12.97
Floodwater	311	0.70	8.08	6.20	6.88	13.77
Public Latrine	612	-0.15	4.45	3.03	3.66	7.31
Soil	628	-1.00	5.60	3.97	4.68	9.35
Bathing Water	210	-0.30	4.38	2.85	3.48	6.97
Street Food	305	0.69	6.55	4.82	5.52	11.04
Other Drinking Water	379	-0.30	3.86	2.37	2.90	5.80

Samples - Membrane Filtration

E. coli count per Sample Types - Membrane Filtration

sample_type_name	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Open Drain Water	343	-0.30	9.58	7.77	8.46	16.92
Raw Produce	305	1.40	7.00	5.44	6.13	12.25
Municipal Drinking Water	335	-0.30	3.30	1.47	2.28	4.55
Ocean	40	1.95	6.30	5.59	5.79	11.59
Surface Water	15	1.48	4.94	4.00	4.39	8.79
Floodwater	174	0.70	7.27	5.91	6.33	12.67
Public Latrine	452	-0.15	4.45	3.16	3.72	7.44
Soil	468	-1.00	5.60	4.05	4.73	9.46
Bathing Water	110	-0.30	3.88	2.44	2.93	5.87
Street Food	155	1.32	6.55	4.73	5.56	11.11
Other Drinking Water	189	-0.30	3.40	2.16	2.62	5.24

Samples - IDEXX

E. coli count per Sample Types - IDEXX

sample_type_name	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Open Drain Water	150	2.00	8.71	7.40	7.78	15.56
Raw Produce	160	1.40	6.06	4.64	5.22	10.44
Municipal Drinking Water	150	-0.30	4.76	3.18	3.79	7.59
Surface Water	110	1.30	7.38	6.04	6.51	13.02
Floodwater	137	1.70	8.08	6.43	7.06	14.12
Public Latrine	160	-0.15	2.80	1.13	1.77	3.55
Soil	160	0.00	5.09	3.57	4.21	8.43
Bathing Water	100	-0.30	4.38	3.06	3.63	7.26
Street Food	150	0.69	6.44	4.91	5.47	10.94
Other Drinking Water	190	-0.30	3.86	2.51	3.01	6.03

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Produce

Contamination of Produce

Differences within Produce Samples

General comparison tables indicating how many samples were taken overall.

Types of Produce and Number of Samples

id	produce	n
1	cabbage	33
2	cucumber	73
3	egg plant	2
4	lettuce	34
5	long bean	2
6	long plant	2
7	salad	14
8	tomato	162
9	water mimosa	1
10	wing bean	2
11	apple	6
12	carrot	11
13	pepper	66
14	spring onion	3
15	guava	4
16	coriander	35
17	green chilly	10
18	okra	3
19	watermelon	1

Types of Produce, by city

city	n	produce
Accra	4	cabbage
Accra	1	cucumber
Accra	6	lettuce
Accra	31	tomato
Accra	1	apple
Accra	3	carrot
Accra	41	pepper
Accra	3	spring onion
Atlanta	1	cucumber
Atlanta	2	lettuce
Atlanta	3	tomato
Atlanta	3	pepper
Dakar	9	cucumber
Dakar	10	salad
Dakar	9	tomato
Dakar	8	carrot
Dakar	9	pepper
Dakar	5	green chilly
Dhaka	35	cucumber
Dhaka	34	tomato
Dhaka	31	coriander
Kampala	25	cabbage
Kampala	25	tomato
Kumasi	13	lettuce
Kumasi	13	tomato
Kumasi	13	pepper
Lusaka	12	cucumber
Lusaka	28	tomato
Lusaka	5	apple
Lusaka	4	guava
Lusaka	1	watermelon
Maputo	7	cucumber
Maputo	8	lettuce
Maputo	8	tomato
Siem Reap	4	cabbage
Siem Reap	8	cucumber
Siem Reap	2	egg plant
Siem Reap	5	lettuce
Siem Reap	2	long bean
Siem Reap	2	long plant
Siem Reap	4	salad
Siem Reap	3	tomato
Siem Reap	1	water mimosa
Siem Reap	2	wing bean
Vellore	8	tomato
Vellore	4	coriander
Vellore	5	green chilly
Vellore	3	okra

E. coli count per Types of Produce

produce	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
apple	6	1.40	2.41	1.81	1.98	3.96
cabbage	33	1.40	5.73	4.68	5.05	10.09
carrot	11	2.40	5.22	4.48	4.71	9.41
coriander	35	2.70	6.06	5.28	5.51	11.02
cucumber	73	1.40	7.00	5.69	6.32	12.64
egg plant	2	4.11	4.83	4.61	4.59	9.19
green chilly	10	2.40	6.00	5.34	5.61	11.21
guava	4	1.40	5.50	5.02	5.26	10.52
lettuce	34	1.40	7.00	5.65	6.24	12.48
long bean	2	2.00	2.48	2.30	2.15	4.30
long plant	2	3.22	3.48	3.37	2.98	5.96
okra	3	3.57	4.10	3.83	3.70	7.39
pepper	66	1.40	6.00	5.18	5.50	11.00
salad	14	2.40	5.82	5.20	5.29	10.58
spring onion	3	2.60	4.60	4.31	4.45	8.89
tomato	162	1.40	7.00	4.99	5.91	11.83
water mimosa	1	4.80	4.80	4.80
watermelon	1	1.40	1.40	1.40
wing bean	2	2.81	4.81	4.51	4.65	9.30
	1	1.40	1.40	1.40

Graphs - Differences within Produce Samples

E. coli count by deployment and neighborhood and type of produce

E. coli count by deployment and neighborhood and type of produce

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Latrines

Latrines Types from Sample Collection

Types of Latrines in all deployments

n	Type
45	Flush/pour flush to pit
310	Flush/pour flush to septic tank
66	Flush/pour flush to sewage system
70	Pit latrine with slab
75	VIP
4	Open pit latrine (without slab)
8	Other

## Warning: Column `col_UID`/`UID` joining factor and character vector, coercing
## into character vector

Types of Latrines by deployment

city	n	Type
Accra	259	Flush/pour flush to septic tank
Accra	20	VIP
Accra	1	Other
Dhaka	6	Flush/pour flush to septic tank
Dhaka	64	Flush/pour flush to sewage system
Dhaka	10	Pit latrine with slab
Dhaka	12	VIP
Dhaka	4	Open pit latrine (without slab)
Dhaka	4	Other
Kampala	4	Flush/pour flush to pit
Kampala	8	Flush/pour flush to septic tank
Kampala	1	Flush/pour flush to sewage system
Kampala	13	Pit latrine with slab
Kampala	23	VIP
Kampala	1	Other
Kumasi	20	Flush/pour flush to septic tank
Kumasi	7	Pit latrine with slab
Lusaka	18	Flush/pour flush to pit
Lusaka	17	Flush/pour flush to septic tank
Lusaka	1	Flush/pour flush to sewage system
Lusaka	11	Pit latrine with slab
Lusaka	3	VIP
Maputo	23	Flush/pour flush to pit
Maputo	29	Pit latrine with slab
Maputo	17	VIP
Maputo	2	Other

Overall E. coli count per Types of Latrines

Type	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Flush/pour flush to pit	45	-0.15	4.45	2.89	3.65	7.29
Flush/pour flush to septic tank	310	-0.15	4.45	3.21	3.76	7.52
Flush/pour flush to sewage system	66	-0.15	3.05	1.58	2.20	4.41
Pit latrine with slab	70	-0.15	3.45	2.30	2.80	5.61
VIP	75	-0.15	4.45	2.98	3.58	7.15
Open pit latrine (without slab)	4	-0.15	1.85	1.26	1.54	3.09
Other	8	-0.15	2.75	1.86	2.29	4.59
	34	-0.15	3.45	2.89	3.06	6.12

E. coli count per Types of Latrines by city

Type	city	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Flush/pour flush to septic tank	Accra	259	-0.15	4.45	3.27	3.80	7.59
VIP	Accra	20	-0.15	4.45	3.52	3.83	7.66
Other	Accra	1	-0.15	-0.15	-0.15
	Atlanta	10	-0.15	-0.15	-0.15	-Inf	-Inf
Flush/pour flush to septic tank	Dhaka	6	-0.15	1.88	1.13	1.49	2.98
Flush/pour flush to sewage system	Dhaka	64	-0.15	2.80	1.33	1.94	3.88
Pit latrine with slab	Dhaka	10	-0.15	2.10	1.45	1.69	3.37
VIP	Dhaka	12	-0.15	1.19	0.30	0.63	1.25
Open pit latrine (without slab)	Dhaka	4	-0.15	1.85	1.26	1.54	3.09
Other	Dhaka	4	-0.15	-0.15	-0.15	-Inf	-Inf
Flush/pour flush to pit	Kampala	4	-0.15	2.75	2.30	2.49	4.99
Flush/pour flush to septic tank	Kampala	8	-0.15	2.92	2.08	2.47	4.93
Flush/pour flush to sewage system	Kampala	1	3.05	3.05	3.05
Pit latrine with slab	Kampala	13	-0.15	3.23	2.43	2.78	5.57
VIP	Kampala	23	-0.15	3.53	2.23	2.85	5.71
Other	Kampala	1	1.29	1.29	1.29
Flush/pour flush to septic tank	Kumasi	20	-0.15	4.05	2.90	3.43	6.85
Pit latrine with slab	Kumasi	7	-0.15	3.10	2.27	2.68	5.35
Flush/pour flush to pit	Lusaka	18	-0.15	1.30	0.33	0.65	1.31
Flush/pour flush to septic tank	Lusaka	17	-0.15	2.28	1.21	1.66	3.33
Flush/pour flush to sewage system	Lusaka	1	-0.15	-0.15	-0.15
Pit latrine with slab	Lusaka	11	-0.15	0.15	-0.08	-0.55	-1.10
VIP	Lusaka	3	-0.15	-0.15	-0.15	-Inf	-Inf
Flush/pour flush to pit	Maputo	23	-0.15	4.45	3.21	3.81	7.61
Pit latrine with slab	Maputo	29	-0.15	3.45	2.48	2.94	5.87
VIP	Maputo	17	-0.15	1.84	0.93	1.28	2.56
Other	Maputo	2	0.45	2.75	2.45	2.60	5.19
	Vellore	24	-0.15	3.45	3.04	3.09	6.18

E. coli count of all latrines by city

city	Neighborhood	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Accra	301	19	-0.15	4.45	3.72	3.94	7.88
Accra	302	95	-0.15	4.02	2.59	3.22	6.45
Accra	303	59	-0.15	4.45	3.56	3.94	7.88
Accra	304	29	-0.15	4.45	3.34	3.87	7.73
Accra	305	58	-0.15	4.45	3.37	3.84	7.69
Accra	601	10	-0.15	1.45	0.55	0.93	1.87
Accra	602	10	-0.15	2.62	1.69	2.12	4.23
Atlanta	1001	10	-0.15	-0.15	-0.15	-Inf	-Inf
Dhaka	201	10	-0.15	1.88	0.92	1.38	2.76
Dhaka	202	10	-0.15	1.55	0.80	1.06	2.13
Dhaka	203	10	-0.15	1.85	0.89	1.34	2.69
Dhaka	204	10	-0.15	2.42	1.50	1.92	3.83
Dhaka	205	10	-0.15	-0.15	-0.15	-Inf	-Inf
Dhaka	206	10	-0.15	2.80	1.89	2.29	4.58
Dhaka	207	10	-0.15	0.76	0.08	0.20	0.40
Dhaka	208	10	-0.15	2.10	1.48	1.68	3.36
Dhaka	209	10	-0.15	2.25	1.27	1.75	3.50
Dhaka	210	10	-0.15	0.76	0.11	0.20	0.40
Kampala	901	10	-0.15	1.83	1.22	1.43	2.86
Kampala	902	10	-0.15	3.23	2.28	2.73	5.46
Kampala	903	10	0.85	3.15	2.61	2.73	5.46
Kampala	904	10	-0.15	-0.15	-0.15	-Inf	-Inf
Kampala	905	10	-0.15	3.53	2.55	3.02	6.05
Kumasi	701	10	0.75	3.10	2.16	2.59	5.19
Kumasi	702	9	-0.15	4.05	3.27	3.60	7.21
Kumasi	703	4	-0.15	1.32	0.97	0.95	1.90
Kumasi	704	4	-0.15	0.62	0.32	0.23	0.47
Lusaka	401	20	-0.15	0.15	-0.11	-0.67	-1.33
Lusaka	1301	10	-0.15	2.28	1.40	1.77	3.55
Lusaka	1302	10	-0.15	1.45	0.59	0.93	1.87
Lusaka	1303	10	-0.15	1.02	0.31	0.50	1.01
Maputo	501	15	-0.15	3.45	2.27	2.86	5.72
Maputo	502	13	-0.15	3.45	2.67	3.02	6.03
Maputo	801	30	-0.15	3.23	2.04	2.55	5.10
Maputo	802	13	-0.15	4.45	3.34	3.89	7.78
Vellore	1101	12	0.15	3.45	3.07	3.10	6.20
Vellore	1102	12	-0.15	3.45	3.02	3.10	6.19

Graphs - Differences within Latrine Types

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Streetfood

Differences within Streetfood

Streetfood is standardized as one serving size of food available from street vendors.

## Warning: Column `col_UID`/`UID` joining factor and character vector, coercing
## into character vector

Streetfood E. coli count per city

city	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Accra	20	1.95	6.55	5.52	5.96	11.93
Dakar	50	2.67	3.83	2.88	3.01	6.01
Dhaka	100	0.69	6.44	5.11	5.56	11.12
Kampala	45	1.54	5.56	4.37	4.86	9.72
Kumasi	40	1.32	4.34	3.12	3.66	7.33
Lusaka	50	0.70	1.99	1.56	1.40	2.80

Streetfood E. coli count per neighborhood

neighb_UID	city	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
201	Dhaka	10	0.69	5.50	4.81	5.08	10.16
202	Dhaka	10	2.32	5.83	5.14	5.34	10.68
203	Dhaka	10	2.01	6.44	5.67	5.99	11.97
204	Dhaka	10	1.67	5.94	5.12	5.52	11.03
205	Dhaka	10	1.22	4.48	3.88	4.04	8.08
206	Dhaka	10	2.52	5.62	4.97	5.22	10.45
207	Dhaka	10	2.04	4.45	3.76	3.98	7.96
208	Dhaka	10	3.53	5.79	5.04	5.32	10.64
209	Dhaka	10	1.80	5.27	4.80	4.93	9.85
210	Dhaka	10	2.27	5.80	5.26	5.37	10.74
401	Lusaka	20	0.70	1.99	1.59	1.53	3.06
601	Accra	10	2.00	6.55	5.64	6.05	12.09
602	Accra	10	1.95	6.37	5.37	5.87	11.73
701	Kumasi	10	1.32	3.16	2.63	2.76	5.51
702	Kumasi	10	1.84	2.30	2.06	1.66	3.33
703	Kumasi	10	1.79	4.34	3.65	3.94	7.87
704	Kumasi	10	1.83	2.97	2.29	2.42	4.84
901	Kampala	10	1.54	5.56	4.92	5.20	10.40
902	Kampala	9	1.91	5.31	4.75	4.90	9.80
903	Kampala	6	1.90	2.26	2.09	1.67	3.34
904	Kampala	10	1.71	3.85	3.34	3.42	6.85
905	Kampala	10	1.70	3.49	2.61	2.98	5.95
1201	Dakar	10	2.67	3.00	2.76	2.24	4.47
1202	Dakar	10	2.67	2.83	2.74	1.95	3.89
1203	Dakar	10	2.67	2.83	2.74	1.94	3.89
1204	Dakar	10	2.67	3.61	2.98	3.04	6.08
1205	Dakar	10	2.67	3.83	3.07	3.30	6.59
1301	Lusaka	10	1.12	1.99	1.61	1.40	2.80
1302	Lusaka	10	1.45	1.84	1.59	1.04	2.09
1303	Lusaka	10	0.98	1.74	1.44	1.14	2.29

Graphs - Differences within Produce Samples

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Drinking Water

Differences within Drinking Water

Municipal Drinking Water E. coli count per deployment

city	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
Accra	88	-0.3	3.30	1.86	2.49	4.98
Atlanta	10	-0.3	-0.30	-0.30	-Inf	-Inf
Dakar	100	-0.3	0.00	-0.30	-1.30	-2.60
Dhaka	100	-0.3	4.76	3.36	3.88	7.75
Kampala	39	-0.3	1.11	-0.01	0.35	0.70
Kumasi	36	-0.3	3.22	1.71	2.44	4.88
Lusaka	40	-0.3	2.27	1.05	1.59	3.17
Maputo	40	-0.3	2.77	1.46	1.99	3.98
Siem Reap	10	-0.3	-0.30	-0.30	-Inf	-Inf
Vellore	22	-0.3	2.30	1.23	1.71	3.42

Municipal Drinking Water E. coli count per neighborhood

neighb_UID	city	n	min ecoli	max ecoli	mean ecoli	standard deviation	variance
104	Siem Reap	10	-0.30	-0.30	-0.30	-Inf	-Inf
201	Dhaka	10	-0.30	1.38	0.70	0.90	1.79
202	Dhaka	10	-0.30	1.71	0.90	1.22	2.44
203	Dhaka	10	-0.30	1.35	0.64	0.86	1.71
204	Dhaka	10	-0.30	4.38	3.48	3.88	7.75
205	Dhaka	10	-0.30	2.18	1.19	1.68	3.36
206	Dhaka	10	-0.30	4.30	3.32	3.80	7.59
207	Dhaka	10	0.78	4.38	3.86	3.99	7.98
208	Dhaka	10	1.56	4.76	4.00	4.24	8.48
209	Dhaka	10	-0.30	2.62	2.18	2.18	4.36
210	Dhaka	10	-0.30	3.41	2.45	2.91	5.81
301	Accra	10	-0.30	1.38	0.95	1.01	2.03
302	Accra	30	-0.30	3.30	2.30	2.71	5.42
303	Accra	9	-0.30	0.00	-0.21	-0.66	-1.31
304	Accra	9	-0.30	0.48	-0.11	-0.08	-0.16
305	Accra	10	-0.30	-0.30	-0.30	-Inf	-Inf
401	Lusaka	10	-0.30	0.30	-0.19	-0.32	-0.65
601	Accra	10	-0.30	1.91	1.00	1.40	2.81
602	Accra	10	-0.30	1.63	0.84	1.12	2.24
701	Kumasi	10	-0.30	1.49	1.08	1.02	2.03
702	Kumasi	10	-0.30	3.22	2.23	2.72	5.44
703	Kumasi	10	-0.30	0.30	-0.05	-0.34	-0.68
704	Kumasi	6	-0.30	1.40	0.66	1.00	2.00
801	Maputo	30	-0.30	2.77	1.53	2.05	4.09
802	Maputo	10	-0.30	1.72	1.10	1.26	2.52
902	Kampala	10	-0.30	-0.30	-0.30	-Inf	-Inf
903	Kampala	9	-0.30	-0.30	-0.30	-Inf	-Inf
904	Kampala	10	-0.30	-0.30	-0.30	-Inf	-Inf
905	Kampala	10	-0.30	1.11	0.38	0.63	1.26
1001	Atlanta	10	-0.30	-0.30	-0.30	-Inf	-Inf
1101	Vellore	11	-0.30	2.00	1.06	1.52	3.04
1102	Vellore	11	-0.30	2.30	1.36	1.82	3.65
1201	Dakar	20	-0.30	-0.30	-0.30	-Inf	-Inf
1202	Dakar	20	-0.30	0.00	-0.28	-0.95	-1.90
1203	Dakar	20	-0.30	-0.30	-0.30	-Inf	-Inf
1204	Dakar	20	-0.30	-0.30	-0.30	-Inf	-Inf
1205	Dakar	20	-0.30	-0.30	-0.30	-Inf	-Inf
1301	Lusaka	10	-0.30	-0.30	-0.30	-Inf	-Inf
1302	Lusaka	10	-0.30	0.00	-0.26	-0.80	-1.60
1303	Lusaka	10	-0.30	2.27	1.64	1.85	3.69

Graphs - Differences within Drinking Water Samples

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Behavior - Comb

Adults - Combined Frequency

Yes/ No Questions

Children - Combined Frequency

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Exposure

Adults

The SaniPath exposure data is displayed here by pathway. The different colors represent the individual deployment, while a dot represents a particular neighborhood.

Children

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Map

Country Map

Neighborhood Map

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The end.